Data-Driven Campaigning and Political Parties
Bibliographic record
Abstract
Abstract What is data-driven campaigning? According to prevailing accounts, this idea describes the rise of increasingly sophisticated, highly targeted, and often invasive uses of data. Deployed to suppress votes, manipulate voter preferences, or boost a candidates’ popularity, the power of data is seen to be transforming campaign practice and raising concerns over democratic processes. And yet, there is a significant problem with these ideas: there is at best a partial understanding of the nature of data-driven campaigning, and limited clarity about its implications. This book provides unprecedented insight into the conduct of data-driven campaigns. Presenting data from interviews with over 300 professional campaigners in Australia, Canada, Germany, the United Kingdom, and the United States, the authors provide unique insight into the components of data-driven campaigning by political parties. They make three key contributions. First, distinguishing between data, analytics, technology, and personnel, they provide unmatched descriptive insight into these four components of data-driven campaigning, revealing significant variation in its operationalization depending on party and country context. Second, introducing a novel multi-level theoretical framework, they isolate systemic, regulatory, and party-level variables which help explain the reasons for these differences. Third, they consider the implications of these findings for debates about democracy, data, and technology in the twenty-first century. Cumulatively these contributions reveal data-driven campaigning to come in different forms that are not inherently problematic. Giving voice to practitioner perspectives, through interviews and innovative vignettes, this book recasts the debate around data-driven campaigning, offering important lessons for scholars, campaigners, and policymakers alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".